Lexicographic Orders of Intuitionistic Fuzzy Values and Their Relationships

Intuitionistic fuzzy multiple attribute decision making deals with the issue of ranking alternatives based on the decision information quantified in terms of intuitionistic fuzzy values. Lexicographic orders can serve as efficient and indispensable tools for comparing intuitionistic fuzzy values. This paper introduces a number of lexicographic orders by means of several measures such as the membership, non-membership, score, accuracy and expectation score functions. Some equivalent characterizations and illustrative examples are provided, from which the relationships among these lexicographic orders are ascertained. We also propose three different compatible properties of preorders with respect to the algebraic sum and scalar product operations of intuitionistic fuzzy values, and apply them to the investigation of compatible properties of various lexicographic orders. In addition, a benchmark problem regarding risk investment is further explored to give a comparative analysis of different lexicographic orders and highlight the practical value of the obtained results for solving real-world decision-making problems.

Towards a Green State: A Comparative Study on OECD Countries through Fuzzy-Set Analysis

This study aims to develop an empirical measurement framework of the green state and compare twenty-four OECD (Organization for Economic Cooperation and Development) countries’ cases through the fuzzy-set multiple conjunctural analysis and the ideal type analysis. Based on the analysis model of the outcome set (Sustainable Development Goal Index) and the causal sets of seven variables on the four green state categories (‘ecological authoritarian state’, ‘ecological modern state’, ‘ecological democracy state’, and ‘ecological welfare state’), this study reveals the following results. Among OECD member countries, if ones have high environmental tax, high environmental innovation (patent), high economic development and democracy, high levels of environmental governance and social expenditure, or have high economic development and democracy, and high levels of environmental governance and environmental health, they can be seen to have reached a high level of green state (consistency: 0.980, total coverage: 0.675). Also, the thirteen ideal types of green state of twenty-four OECD countries were derived. Norway (fuzzy-set membership score of 0.515) is a country of Type 1, with a characteristic of ‘strong green state’ having all high features of the four green state categories. Greece (membership score, 0.692) and Ireland (0.577) belong to Type 13, characterized by ‘weak green state’ with all four low features. As a result, the green state types of the twenty-four OECD countries can be assorted into five levels: ‘Strong Green State’, ‘Quasi-Strong Green State’, ‘Quasi-Green State’, ‘Quasi-Weak Green State’, and ‘Weak Green State’.

A semantic approach for text document clustering using frequent itemsets and WordNet

Document Clustering is an unsupervised method for classified documents in clusters on the basis of their similarity. Any document get it place in any specific cluster, on the basis of membership score, which calculated through membership function. But many of the traditional clustering algorithms are generally based on only BOW (Bag of Words), which ignores the semantic similarity between document and Cluster. In this research we consider the semantic association between cluster and text document during the calculation of membership score of any document for any specific cluster. Several researchers are working on semantic aspects of document clustering to develop clustering performance. Many external knowledge bases like WordNet, Wikipedia, Lucene etc. are utilized for this purpose. The proposed approach exploits WordNet to improve cluster member ship function. The experimental result shows that clustering quality improved significantly by using proposed framework of semantic approach.

Land suitability assessment in the Namoi Valley of Australia, using a continuous model

In an agricultural context, land evaluation is assessment for a specified kind of land utilisation. The final result of agricultural evaluation is a map, which partitions the landscapes into suitable and unsuitable areas for a particular land-use of interest. However, this approach may not represent the continuity of land. Land suitability could be better expressed by a fuzzy approach. In this paper a fuzzy methodology is used to evaluate land suitability in the Edgeroi district for various crops including barley, dryland cotton, oats, pasture, soybean, sorghum, sunflower, and wheat. This is achieved using a membership function to derive a land-suitability membership score ranging from non-suitable (i.e. 0) to suitable (i.e. 1). We express this as continuous land suitability maps using punctual kriging. An expression for overall land suitability (i.e. its versatility) and its capacity with respect to suitability to particular rotations is introduced to highlight the most productive units of soil.